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16
Ensemble Methods in Machine Learning
 MULTIPLE CLASSIFIER SYSTEMS, LBCS1857
, 2000
"... Ensemble methods are learning algorithms that construct a set of classifiers and then classify new data points by taking a (weighted) vote of their predictions. The original ensemble method is Bayesian averaging, but more recent algorithms include errorcorrecting output coding, Bagging, and boostin ..."
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Cited by 426 (3 self)
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Ensemble methods are learning algorithms that construct a set of classifiers and then classify new data points by taking a (weighted) vote of their predictions. The original ensemble method is Bayesian averaging, but more recent algorithms include errorcorrecting output coding, Bagging, and boosting. This paper reviews these methods and explains why ensembles can often perform better than any single classifier. Some previous studies comparing ensemble methods are reviewed, and some new experiments are presented to uncover the reasons that Adaboost does not overfit rapidly.
Object Detection in Images by Components
, 1999
"... In this paper we present a component based person detection system that is capable of detecting frontal, rear and near side views of people, and partially occluded persons in cluttered scenes. The framework that is described here for people is easily applied to other objects as well. The motivatio ..."
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Cited by 235 (11 self)
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In this paper we present a component based person detection system that is capable of detecting frontal, rear and near side views of people, and partially occluded persons in cluttered scenes. The framework that is described here for people is easily applied to other objects as well. The motivation for developing a component based approach istwofold: rst, to enhance the performance of person detection systems on frontal and rear views of people and second, to develop a framework that directly addresses the problem of detecting people who are partially occluded or whose body parts blend in with the background. The data classi cation is handled by several support vector machine classi ers arranged in two layers. This architecture is known as Adaptive Combination of Classi ers (ACC). The system performs very well and is capable of detecting people even when all components of a person are not found. The performance of the system is signi cantly better than a full body
Tree Induction for Probabilitybased Ranking
, 2002
"... Tree induction is one of the most effective and widely used methods for building classification models. However, many applications require cases to be ranked by the probability of class membership. Probability estimation trees (PETs) have the same attractive features as classification trees (e.g., c ..."
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Cited by 130 (4 self)
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Tree induction is one of the most effective and widely used methods for building classification models. However, many applications require cases to be ranked by the probability of class membership. Probability estimation trees (PETs) have the same attractive features as classification trees (e.g., comprehensibility, accuracy and efficiency in high dimensions and on large data sets). Unfortunately, decision trees have been found to provide poor probability estimates. Several techniques have been proposed to build more accurate PETs, but, to our knowledge, there has not been a systematic experimental analysis of which techniques actually improve the probabilitybased rankings, and by how much. In this paper we first discuss why the decisiontree representation is not intrinsically inadequate for probability estimation. Inaccurate probabilities are partially the result of decisiontree induction algorithms that focus on maximizing classification accuracy and minimizing tree size (for example via reducederror pruning). Larger trees can be better for probability estimation, even if the extra size is superfluous for accuracy maximization. We then present the results of a comprehensive set of experiments, testing some straghtforward methods for improving probabilitybased rankings. We show that using a simple, common smoothing methodthe Laplace correctionuniformly improves probabilitybased rankings. In addition, bagging substantioJly improves the rankings, and is even more effective for this purpose than for improving accuracy. We conclude that PETs, with these simple modifications, should be considered when rankings based on classmembership probability are required.
Linear programming boosting via column generation
 Machine Learning
, 2002
"... 1 Introduction Recent papers [20] have shown that boosting, arcing, and related ensemble methods (hereafter summarized asboosting) can be viewed as margin maximization in function space. By changing the cost function, different ..."
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Cited by 101 (3 self)
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1 Introduction Recent papers [20] have shown that boosting, arcing, and related ensemble methods (hereafter summarized asboosting) can be viewed as margin maximization in function space. By changing the cost function, different
Learning and Making Decisions When Costs and Probabilities are Both Unknown
 In Proceedings of the Seventh International Conference on Knowledge Discovery and Data Mining
, 2001
"... In many machine learning domains, misclassication costs are dierent for dierent examples, in the same way that class membership probabilities are exampledependent. In these domains, both costs and probabilities are unknown for test examples, so both cost estimators and probability estimators must be ..."
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Cited by 96 (9 self)
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In many machine learning domains, misclassication costs are dierent for dierent examples, in the same way that class membership probabilities are exampledependent. In these domains, both costs and probabilities are unknown for test examples, so both cost estimators and probability estimators must be learned. This paper rst discusses how to make optimal decisions given cost and probability estimates, and then presents decision tree learning methods for obtaining wellcalibrated probability estimates. The paper then explains how to obtain unbiased estimators for exampledependent costs, taking into account the diculty that in general, probabilities and costs are not independent random variables, and the training examples for which costs are known are not representative of all examples. The latter problem is called sample selection bias in econometrics. Our solution to it is based on Nobel prizewinning work due to the economist James Heckman. We show that the methods we propose are s...
Tree induction vs. logistic regression: A learningcurve analysis
 CEDER WORKING PAPER #IS0102, STERN SCHOOL OF BUSINESS
, 2001
"... Tree induction and logistic regression are two standard, offtheshelf methods for building models for classi cation. We present a largescale experimental comparison of logistic regression and tree induction, assessing classification accuracy and the quality of rankings based on classmembership pr ..."
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Cited by 62 (16 self)
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Tree induction and logistic regression are two standard, offtheshelf methods for building models for classi cation. We present a largescale experimental comparison of logistic regression and tree induction, assessing classification accuracy and the quality of rankings based on classmembership probabilities. We use a learningcurve analysis to examine the relationship of these measures to the size of the training set. The results of the study show several remarkable things. (1) Contrary to prior observations, logistic regression does not generally outperform tree induction. (2) More specifically, and not surprisingly, logistic regression is better for smaller training sets and tree induction for larger data sets. Importantly, this often holds for training sets drawn from the same domain (i.e., the learning curves cross), so conclusions about inductionalgorithm superiority on a given domain must be based on an analysis of the learning curves. (3) Contrary to conventional wisdom, tree induction is effective atproducing probabilitybased rankings, although apparently comparatively less so foragiven training{set size than at making classifications. Finally, (4) the domains on which tree induction and logistic regression are ultimately preferable canbecharacterized surprisingly well by a simple measure of signaltonoise ratio.
Boosting Applied to Word Sense Disambiguation
 IN PROCEEDINGS OF THE 12TH EUROPEAN CONFERENCE ON MACHINE LEARNING
, 2000
"... In this paper Schapire and Singer's AdaBoost.MH boosting algorithm is applied to the Word Sense Disambiguation (WSD) problem. Initial experiments on a set of 15 selected polysemous words show that the boosting approach surpasses Naive Bayes and Exemplarbased approaches, which represent stateof ..."
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Cited by 54 (8 self)
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In this paper Schapire and Singer's AdaBoost.MH boosting algorithm is applied to the Word Sense Disambiguation (WSD) problem. Initial experiments on a set of 15 selected polysemous words show that the boosting approach surpasses Naive Bayes and Exemplarbased approaches, which represent stateoftheart accuracy on supervised WSD. In order to make boosting practical for a real learning domain of thousands of words, several ways of accelerating the algorithm by reducing the feature space are studied. The best variant, which we call LazyBoosting, is tested on the largest sensetagged corpus available containing 192,800 examples of the 191 most frequent and ambiguous English words. Again, boosting compares favourably to the other benchmark algorithms.
Lightweight Rule Induction
, 2000
"... A lightweight rule induction method is described that generates compact Disjunctive Normal Form (DNF) rules. Each class has an equal numberofunweighted rules. A new example is classified by applying all rules and assigning the example to the class with the most satisfied rules. The induction m ..."
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Cited by 13 (1 self)
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A lightweight rule induction method is described that generates compact Disjunctive Normal Form (DNF) rules. Each class has an equal numberofunweighted rules. A new example is classified by applying all rules and assigning the example to the class with the most satisfied rules. The induction method attempts to minimize the training error with no pruning. An overall design is specified by setting limits on the size and number of rules. During training, cases are adaptively weighted using a simple cumulativeerror method. The induction method is nearly linear in time relative to an increase in the number of induced rules or the number of cases. Experimental results on large benchmark data sets demonstrate that predictive performance can rival the best reported results in the literature.
An Empirical Study of MetaCost using Boosting Algorithms
 In: Proceedings of the Eleventh European Conference on Machine Learning
"... MetaCost is a recently proposed procedure that converts an errorbased learning algorithm into a costsensitive algorithm. This paper investigates two important issues centered on the procedure which were ignored in the paper proposing MetaCost. First, no comparison was made between MetaCost's final ..."
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Cited by 12 (1 self)
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MetaCost is a recently proposed procedure that converts an errorbased learning algorithm into a costsensitive algorithm. This paper investigates two important issues centered on the procedure which were ignored in the paper proposing MetaCost. First, no comparison was made between MetaCost's final model and the internal costsensitive classifier on which MetaCost depends. It is credible that the internal costsensitive classifier may outperform the final model without the additional computation required to derive the final model. Second, MetaCost assumes its internal costsensitive classifier is obtained by applying a minimum expected cost criterion. It is unclear whether violation of the assumption has an impact on MetaCost's performance. We study these issues using two boosting procedures, and compare with the performance of the original form of MetaCost which employs bagging.